Breast Asymmetry, Distortion and Density Are Key Factors for False Positive Decisions

  • Zoey Z. Y. Ang
  • Rob Heard
  • Mohammad A. Rawashdeh
  • Patrick C. Brennan
  • Warwick Lee
  • Sarah J. Lewis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Aim: Understanding both normal mammographic appearance and how false positive (FP) errors occur is paramount to improving the efficiency and diagnostic accuracy of screening mammography services. While much of the focus of research is on increasing knowledge about the appearances and imaging of breast cancers, this study reports on findings where breast screen readers are asked to comment on past incorrect decisions by assigning a lexicon that best describes a known FP region. Method: Fifteen breast screen readers were given two tasks. The first was to assess nine normal screening cases which had attracted a high number of FP decisions in a test set of 60 cases in a previous study with 129 readers. In the second task, the 15 readers in this study, who were made aware that the nine cases were normal, were directed to view distinct regions of interest (ROI) that represented the FP markings from past readings in the blinded observer performance study. A list of descriptors derived from literature was used to assist readers to describe the mammographic appearance within those ROIs. Results: In the first task, readers identified breast density as the greatest difficulty in determining normality. In the second task, asymmetry of breast tissue and a suspicion of architectural distortion (AD) were the top two reasons our readers gave to explain the high number of past FP decisions. Additionally, our readers believed past FP decisions were less likely to reflect a suspicion of breast lesions or masses (second task). Conclusion: The classification of normal cases remains a challenging task, influenced by asymmetry and breast density. FP decisions may reflect a suspicion of AD and appear less related to suspicion of masses.

Keywords

Mammography Normal mammograms False positive Breast density Breast tissue asymmetry 

Notes

Acknowledgements

This study was supported by the National Breast Cancer Foundation (Australia), the Breastscreen Reader Assessment Strategy (BREAST), RANZCR, The University of Sydney and National Healthcare Group Diagnostic (Singapore).

References

  1. 1.
    Australian Institute of Health and Welfare: BreastScreen Australia monitoring report 2011–2012 (2014). http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=60129548882. Accessed 15 May 2015
  2. 2.
    Australian Institute of Health and Welfare: Australian Cancer Incidence and Mortality (ACIM) books, Breast cancer (2015). http://www.aihw.gov.au/acim-books/. Accessed 10 Apr 2015
  3. 3.
    National Cancer Institute: Breast Cancer Screening (PDQ®) - Breast Cancer Screening Concepts (2015). http://www.cancer.gov/cancertopics/pdq/screening/breast/healthprofessional/Page4#_49. Accessed 11 Apr 2015
  4. 4.
    Lee, W.B., Peters, G.: Mammographic screening for breast cancer: a review. J. Med. Radiat. Sci. 60(1), 35–39 (2013). doi:10.1002/jmrs.6 CrossRefGoogle Scholar
  5. 5.
    Rawashdeh, M.A., Bourne, R.M., Ryan, E.A., Lee, W.B., Pietrzyk, M.W., Reed, W.M., Borecky, N., Brennan, P.C.: Quantitative measures confirm the inverse relationship between lesion spiculation and detection of breast masses. Acad. Radiol. 20(5), 576–580 (2013). doi:10.1016/j.acra.2012.12.010 CrossRefGoogle Scholar
  6. 6.
    American College of Radiology: ACR BI-RADS® Atlas Fifth Edition, Quick reference (2013). http://www.acr.org/~/media/ACR/Documents/PDF/QualitySafety/Resources/BIRADS/Posters/BIRADS%20Reference%20Card_web_F.pdf. Accessed 2 Apr 2015
  7. 7.
    D’Orsi, C.J., Kopans, D.B.: Mammographic feature analysis. Semin. Roentgenol. 28(3), 204–230 (1993). doi:10.1016/S0037-198X(05)80080-X CrossRefGoogle Scholar
  8. 8.
    De Paredes, E.S.: Atlas of Mammography, 3rd edn. Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia (2007)Google Scholar
  9. 9.
    Guinebretière, J.M., Menet, E., Tardivon, A., Cherel, P., Vanel, D.: Normal and pathological breast, the histological basis. Eur. J. Radiol. 54(1), 6–14 (2005). doi:10.1016/j.ejrad.2004.11.020 CrossRefGoogle Scholar
  10. 10.
    Kopans, D.B.: Breast Imaging, 3rd edn. Lippincott Williams & Wilkins, Baltimore (2006)Google Scholar
  11. 11.
    Shetty, M.: Breast Cancer Screening and Diagnosis, pp. 119–130. Springer, New York (2014)Google Scholar
  12. 12.
    Stines, J., Tristant, H.: The normal breast and its variations in mammography. Eur. J. Radiol. 54(1), 26–36 (2005). doi:10.1016/j.ejrad.2004.11.017 CrossRefGoogle Scholar
  13. 13.
    Ang Z.Z.Y., Rawashdeh, M.A., Heard, R., Brennan, P.C., Lee, W.B., Lewis, S.J.: The classification of normal screening mammograms. In: SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, vol. 9787, 97870I (2016). doi:10.1117/12.2216626
  14. 14.
    Suleiman, W.I., McEntee, M.F., Lewis, S.J., Rawashdeh, M.A., Georgian-Smith, D., Heard, R., Brennan, P.C.: In the digital era, architectural distortion remains a challenging radiological task. Clin. Radiol. 71(1), e35 (2016)CrossRefGoogle Scholar
  15. 15.
    Ayres, F.J., Rangayvan, R.M.: Characterization of architectural distortion in mammograms. IEEE Eng. Med. Biol. Mag. 24(1), 59–67 (2005). doi:10.1109/MEMB.2005.1384102 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zoey Z. Y. Ang
    • 1
  • Rob Heard
    • 2
  • Mohammad A. Rawashdeh
    • 3
  • Patrick C. Brennan
    • 4
  • Warwick Lee
    • 4
  • Sarah J. Lewis
    • 4
  1. 1.National Healthcare Group DiagnosticsSingaporeSingapore
  2. 2.Health Systems and Global Populations Research Group, Faculty of Health SciencesThe University of SydneySydneyAustralia
  3. 3.Faculty of Applied Medical SciencesJordan University of Science and TechnologyIrbidJordan
  4. 4.Medical Imaging Optimisation and Perception Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health SciencesThe University of SydneySydneyAustralia

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